Understanding Large Language Models -- A Transformative Reading List
Since transformers have such a big impact on everyone's research agenda, I wanted to flesh out a short reading list for machine learning researchers and ...
Large language models have taken the public attention by storm – no pun intended. In just half a decade large language models – transformers – have almost completely changed the field of natural language processing. Moreover, they have also begun to revolutionize fields such as computer vision and computational biology. Since transformers have such a big impact on everyone’s research agenda, I wanted to flesh out a short reading list (an extended version of my comment yesterday ) for machine learning researchers and practitioners getting started. The following list below is meant to be read mo
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related reading
- Understanding Large Language Modelsmagazine.sebastianraschka.com
- GitHub - Hannibal046/Awesome-LLM: Awesome-LLM: a curated list of Large Language Modelgithub.com
- Transformers from Scratchbrandonrohrer.com
- A Mathematical Framework for Transformer Circuitstransformer-circuits.pub
- LLM Resourcesforrestbicker.com
- The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
- A History of Large Language Modelsgregorygundersen.com
- transformer_attention.pdfarxiv.org
- Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
- Getting Caught Up to Modern LLM Research | Samarth Goeldev.samarthgoel.com
- Transformers from Scratche2eml.school
- AI Revolution - Transformers and Large Language Models (LLMs)blog.eladgil.com